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Human mortality shows a pronounced temperature dependence. The minimum mortality temperature (MMT) as a characteristic point of the temperature-mortality relationship is influenced by many factors. As MMT estimates are based on case studies, they are sporadic, limited to data-rich regions, and their drivers have not yet been clearly identified across case studies. This impedes the elaboration of spatially comprehensive impact studies on heat-related mortality and hampers the temporal transfer required to assess climate change impacts. Using 400 MMTs from cities, we systematically establish a generalised model that is able to estimate MMTs (in daily apparent temperature) for cities, based on a set of climatic, topographic and socio-economic drivers. A sigmoid model prevailed against alternative model setups due to having the lowest Akaike Information Criterion (AICc) and the smallest RMSE. We find the long-term climate, the elevation, and the socio-economy to be relevant drivers of our MMT sample within the non-linear parametric regression model. A first model application estimated MMTs for 599 European cities ( >100 000 inhabitants) and reveals a pronounced decrease in MMTs (27.8-16 degrees C) from southern to northern cities. Disruptions of this pattern across regions of similar mean temperatures can be explained by socio-economic standards as noted for central eastern Europe. Our alternative method allows to approximate MMTs independently from the availability of daily mortality records. For the first time, a quantification of climatic and non-climatic MMT drivers has been achieved, which allows to consider changes in socio-economic conditions and climate. This work contributes to the comparability among MMTs beyond location-specific and regional limits and, hence, towards a spatially comprehensive impact assessment for heat-related mortality.
There is a growing recognition that a transition to a sustainable low-carbon society is urgently needed. This transition takes place at multiple and complementary scales, including bottom-up approaches such as community-based initiatives (CBIs). However, empirical research on CBIs has focused until now on anecdotal evidence and little work has been done to quantitatively assess their impact in terms of greenhouse gas (GHG) emissions. In this paper, we analyze 38 European initiatives across the food, energy, transport, and waste sectors to address the following questions: How can the GHG reduction potential of CBIs be quantified and analyzed in a systematic manner across different sectors? What is the GHG mitigation potential of CBIs and how does the reduction potential differ across domains? Through the comparison of the emission intensity arising from the goods and services the CBIs provide in relation to a business-as-usual scenario, we present the potential they have across different activities. This constitutes the foundational step to upscaling and further understanding their potential contribution to achieving climate change mitigation targets. Our findings indicate that energy generation through renewable sources, changes in personal transportation, and dietary change present by far the highest GHG mitigation activities analyzed, since they reduce the carbon footprint of CBI beneficiaries by 24%, 11%, and 7%, respectively. In contrast, the potential for some activities, such as locally grown organic food, is limited. The service provided by these initiatives only reduces the carbon footprint by 0.1%. Overall, although the proliferation of CBIs is very desirable from a climate change mitigation perspective it is necessary to stress that bottom-up initiatives present other important positive dimensions besides GHG mitigation. These initiatives also hold the potential of improving community resilience by strengthening local economies and enhancing social cohesion.